Information recommendation method, apparatus, and server based on user data in an online forum

ABSTRACT

The present disclosure discloses a method, apparatus, and server for information recommendation. Search behavior data, browsing behavior data, and click behavior data on recommended content of a specified user in a forum are acquired. A preprocessing on the search behavior data, the browsing behavior data, and the click behavior data on recommended content is performed respectively to obtain a first recommendation result, a second recommendation result, and a third recommendation result. Distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result are performed according to weights to obtain recommended content to be recommended to the specified user. Search behavior data, browsing behavior data, and click behavior data on recommended content are taken into comprehensive consideration, data used in recommendation is enriched, and accuracy of recommendation is improved.

RELATED APPLICATIONS

This application is a continuation of PCT Application No.PCT/CN2013/084563, filed on Sep. 29, 2013, which claims priority toChinese Patent Application No. 201210377563.3, entitled “INFORMATIONRECOMMENDATION METHOD AND APPARATUS” filed on Oct. 8, 2012, all of whichare incorporated herein by reference in their entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates to the field of the Internet and computertechnology, and more particularly, relates to a method, apparatus andserver for information recommendation.

BACKGROUND

With the development of social networks, various forum communitiesappear. A group of users having similar interest may gather in a sameforum community and discuss various recent popular topics in varioussections of the forum. In a forum, the most fundamental objectives of auser are to read content and acquire information. Therefore, to providethe user with more content or information, when the user is readingcontent, contents of related subjects may be automatically recommendedto the user. It is convenient for the user to acquire relateinformation, and to improve the stickiness of the website and aclick-through rate of a website.

An existing content recommendation method is based on browsing behaviorof a user. In the method, it is assumed that users browse the samecontent have the same interest, browsing behavior of users in a forum isanalyzed to establish a two-dimensional matrix for user andbrowsing-content, a degree of correlation between contents is calculatedbased on this matrix by using an algorithm such as coordinatedfiltering, to obtain a recommendation result, and to recommend therecommendation result to a user.

However, existing technologies at least have the following problems. Inone forum, a same user may be interested in various aspects, andbrowsing behavior of the user may cover contents of different subjects.A simple assumption that users that browse the same content have thesame interest in existing technologies results in that contents ofdifferent subjects are regarded to be close subjects. A recommendationresult obtained in this way is not necessarily the content that a useris interested in, which reduces accuracy of recommended content comparedwith the content that the user is interested in.

In addition, when a forum has a relatively small amount of data, and auser also has a relatively small amount of browsing behavior data, thetwo-dimensional matrix of user and browsing content may becomerelatively scarce, which severely affects a final recommendation effect.Therefore, a recommendation result that is purely obtained from browsingbehavior of a user is not necessarily accurate for the user, andtherefore accuracy of a recommendation result from a forum community toa user is affected.

Therefore, there is a need to solve technical problems in the Internetand computer technology to improve accuracy for recommending contents orinformation to a user in a forum.

BRIEF SUMMARY OF THE DISCLOSURE

One aspect or embodiment of the present disclosure includes aninformation recommendation method. Search behavior data, browsingbehavior data, and click behavior data on recommended content of aspecified user in a forum are acquired. A preprocessing on the searchbehavior data, the browsing behavior data, and the click behavior dataon recommended content is performed respectively to obtain a firstrecommendation result, a second recommendation result, and a thirdrecommendation result. Distribution and integration on the firstrecommendation result, the second recommendation result, and the thirdrecommendation result are performed according to weights to obtainrecommended content to be recommended to the specified user.

Another aspect or embodiment of the present disclosure includes aninformation recommendation apparatus. The apparatus includes: anacquisition module, a preprocessing module, and an integration module.The acquisition module is configured to acquire search behavior data,browsing behavior data, and click behavior data on recommended contentof a specified user in a forum. The preprocessing module is configuredto perform preprocessing on the search behavior data, the browsingbehavior data, and the click behavior data on recommended contentrespectively to obtain a first recommendation result, a secondrecommendation result, and a third recommendation result. Theintegration module is configured to perform distribution and integrationon the first recommendation result, the second recommendation result,and the third recommendation result according to weights, to obtainrecommended content to be recommended to the specified user.

Another aspect or embodiment of the present disclosure includes aserver. The server includes one or more processors and a non-transitorycomputer-readable storage medium having one or more programs storedthereon. The one or more programs are executed by the one or moreprocessors, and include instructions for performing followingoperations. Search behavior data, browsing behavior data, and clickbehavior data on recommended content of a specified user in a forum areacquired. A preprocessing on the search behavior data, the browsingbehavior data, and the click behavior data on recommended content isperformed respectively to obtain a first recommendation result, a secondrecommendation result, and a third recommendation result. Distributionand integration on the first recommendation result, the secondrecommendation result, and the third recommendation result are performedaccording to weights to obtain recommended content to be recommended tothe specified user.

Other aspects or embodiments of the present disclosure can be understoodby those skilled in the art in light of the description, the claims, andthe drawings of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings are merely examples for illustrative purposesaccording to various disclosed embodiments and are not intended to limitthe scope of the present disclosure. The embodiments of the presentinvention are described below with reference to the accompanyingdrawings. In these accompanying drawings:

FIG. 1 is a flowchart of an exemplary information recommendation methodconsistent with various disclosed embodiments;

FIG. 2 is a flowchart of another exemplary information recommendationmethod consistent with various disclosed embodiments;

FIG. 3 is a schematic structural diagram of an exemplary informationrecommendation apparatus consistent with various disclosed embodiments;

FIG. 4 is a schematic structural diagram of another exemplaryinformation recommendation apparatus consistent with various disclosedembodiments; and

FIG. 5 is a schematic structural diagram of an exemplary serverconsistent with various disclosed embodiments.

DESCRIPTION OF EMBODIMENTS

To make the objectives, technical solutions, and advantages in thepresent disclosure clearer, the following further describes theimplementation manners of the present disclosure in detail withreference to the accompanying drawings.

FIGS. 1-5 illustrate exemplary methods, apparatus, and servers forinformation recommendation consistent with various disclosedembodiments.

Exemplary Embodiment 1

FIG. 1 illustrates an exemplary information recommendation methodconsistent with various disclosed embodiments.

In Step 101: search behavior data, browsing behavior data, and clickbehavior data on recommended content of a specified user in a forum areacquired.

In Step 102: a preprocessing on the search behavior data, the browsingbehavior data, and the click behavior data on the recommended content isperformed respectively to obtain a first recommendation result, a secondrecommendation result, and a third recommendation result.

In Step 103: distribution and integration on the first recommendationresult, the second recommendation result, and the third recommendationresult are performed according to weights, to obtain recommendingcontent to be recommended to the specified user.

To preprocess the search behavior data to obtain the firstrecommendation result, noise data in the search behavior data arefiltered out to obtain noise-cancelled data. The noise-cancelled datacan include a query string input in the forum by the specified user.Number of clicks triggered by each query string Qi on a post Ti iscounted. According to the number of clicks triggered by each querystring Qi on the post Ti, a click probability wi triggered by each querystring on the post Ti is calculated. According to the click probabilitywi triggered by each query string on the post Ti, a query vector ftrtriggering a click on the post Ti is established, where the query vectoris <w1, w2, . . . , wi, . . . , wn>. According to the query vectorestablished for each post, a relevance degree between any two posts iscalculated to obtain the first recommendation result.

In one embodiment, to preprocess the browsing behavior data to obtainthe second recommendation result, a post browsed by the specified userin the forum is acquired by analyzing the browsing behavior data.According to a section containing the browsed post, browsing behavior ofthe specified user is categorized into at least one parent subjectsegment. Text similarities between titles of posts in each parentsubject segment are calculated to obtain boundaries between childsubject segments in each parent subject segment. According to theboundaries between the child subject segments of a corresponding parentsubject segment, the corresponding parent subject segment is categorizedto obtain at least one child subject segment. According to each childsubject segment, a browsing behavior matrix of the specified user isestablished. The browsing behavior matrix includes a child subject and anumber of appearing times that posts in the forum appear in the childsubject. A relevance degree of the browsing behavior matrix iscalculated to obtain the second recommendation result.

To preprocess the click behavior data to obtain the third recommendationresult, the click behavior data are analyzed to obtain related posts ofeach post in the forum and to obtain the number of times that eachrelated post is clicked. According to the number of times that eachrelated post is clicked and a relationship between a click time when therelated post is clicked and a current time, the related posts in theforum are analyzed, so that a related post, having a most number ofclicks and having a difference value between the click time and thecurrent time within a preset range, is sorted in the front of a queue,to obtain the third recommendation result.

Optionally, to perform distribution and integration on the firstrecommendation result, the second recommendation result, and the thirdrecommendation result according to weights, to obtain recommendingcontent to be recommended to the specified user: a first averageprobability, a second average probability, and a third averageprobability that related posts of each post in the forum appear in thefirst recommendation result, the second recommendation result, and thethird recommendation result are calculated respectively. According tothe first average probability, the second average probability, and thethird average probability, the weight of the first recommendationresult, the weight of the second recommendation result, and the weightof the third recommendation result are determined respectively.

Based on the weight of the first recommendation result, the weight ofthe second recommendation result, and the weight of the thirdrecommendation result, distribution and integration are performed on thefirst recommendation result, the second recommendation result, and thethird recommendation result, to obtain the recommending content to berecommended to the specified user. Alternatively, according to a presetfirst weight of the first recommendation result, a preset second weightof the second recommendation result, and a preset third weight of thethird recommendation result, distribution and integration are performedon the first recommendation result, the second recommendation result,and the third recommendation result, to obtain the recommending contentto be recommended to the specified user.

As such, search behavior data, browsing behavior data, and clickbehavior data on recommended content of a specified user in a forum areacquired. Preprocessing is performed on the search behavior data, thebrowsing behavior data, and the click behavior data on the recommendedcontent respectively to obtain a first recommendation result, a secondrecommendation result, and a third recommendation result. Distributionand integration are performed on the first recommendation result, thesecond recommendation result, and the third recommendation result, toobtain recommending content to be recommended to the specified user.Search behavior data, browsing behavior data, and click behavior data onrecommended content are taken into comprehensive consideration, dataused in recommendation is enriched, and accuracy of recommendation isimproved.

Exemplary Embodiment 2

An embodiment of the present invention provides an informationrecommendation method. In a forum, fundamental behavior of a userincludes: browsing behavior, search behavior, and click behavior onrecommended content. In one embodiment, a search behavior model, abrowsing behavior model, and a recommendation click behavior model areestablished. By using these three models, three types of behavior dataof the user in the forum are analyzed respectively to obtain threedifferent recommendation results. Finally, the three differentrecommendation results are integrated or otherwise rearranged to obtainfinal recommended content.

FIG. 2 illustrates another exemplary information recommendation methodconsistent with various disclosed embodiments.

In Step 201: search behavior data of a specified user in a forum areacquired. Preprocessing on the search behavior data is performed toobtain a first recommendation result.

For example, the forum may be any forum community on a network or socialnetwork, and the specified user may be any user using the forum. Anyforum and users can be included and not limited in the presentdisclosure.

In one embodiment, during modeling of search behavior, data in the forumare analyzed to acquire the search behavior data of the specified userin the forum. The search behavior data includes search click behaviordata. Preprocessing is first performed on the search click behavior datato filter out noise data of a malicious click. Next, a mapping from aquery string to a clicked post is established. Based on such a mappingrelationship, the post is denoted as a query vector to further calculatesubject relevance degree of subject of the post to obtain arecommendation result.

The noise data may refer to normal operation behavior data of anon-forum user including, for example, those from robot crawling,malicious clicks, and/or other un-normal data. In various embodiments,the noise data may include any non-related data without limitation.Noise data is distributed in terms of time in certain modes. Forexample, when first several pages of search results of one query areclicked by a same user within a sufficiently short time, this clickedevent may be noise data. By using these modes, noise data may befiltered out to obtain clean, noise-cancelled data.

In this exemplary step, to preprocess the search behavior data to obtainthe first recommendation result, noise data in the search behavior dataare filtered out to obtain noise-cancelled data. The noise-cancelleddata may include all query strings input in the forum by the specifieduser. The number of clicks triggered by each query string Qi on a postTi is counted, where c(Qi,T) is the number of clicks triggered by aquery Qi on a post T. According to the number of clicks triggered byeach query string Qi on the post Ti, a click probability wi triggered byeach query string on the post Ti is calculated, wherewi=c(Qi,T)/c(Q1,T)+c(Q2,T)+ . . . +c(Qn,T)), and n is the total numberof query strings. According to the click probability wi triggered byeach query string on the post Ti, a query vector for triggering a clickon the post Ti is established, where the query vector is <w1, w2, . . ., wi, . . . , wn>. According to a query vector established for eachpost, a relevance degree between any two posts is calculated to obtainthe first recommendation result.

In one embodiment, when the specified user performs a search/query byusing a same query string, different search results are output. Thespecified user may click different posts at each time of search.Therefore, each post may be denoted by a query vector for triggering aclick on the post according to click data of the post corresponding to aquery string. For a post T, a query vector of the post T is: T=<w₁, w₂,. . . , w_(i), . . . , w_(n)>. In this manner, each post may be denotedby the query vector. For two posts T_(i) and T_(j), a similarity (orsimilarity degree) between query vectors corresponding to the two postsT_(i) and T_(j) may be used to measure relevance degree of the subjectbetween the two posts T_(i) and T_(j). For example, T_(i)=<w₁, w₂, w₃, .. . , w_(n)>, and T_(j)=<v₁, v₂, v₃, . . . , v_(n)>, and a cosinedistance, that is, a cosine of an included angle between the twovectors, is usually used in the calculation of relevance. Then thesimilarity (or similarity degree) is Sim<T_(i),T_(j)>=(T_(i)*T_(j))/(|T_(i)∥T_(j)|).

In one embodiment, during the calculation of a similarity between queryvectors, a classic vector space model may be used to performcalculation. Certainly, other suitable methods for calculating a vectorsimilarity may also be used, and are not limited in present disclosure.

In Step 202: browsing behavior data of the specified user in the forumare acquired. Preprocessing on the browsing behavior data is performedto obtain a second recommendation result.

For example, during modeling of browsing behavior of a user,characteristics of browsing behavior of the specified user in the forumneed to be analyzed first. And according to the characteristics,preprocessing is performed on the browsing behavior of the user, so thatdata after processing may genuinely reflect a relationship betweeninterest of the user and content of posts. In a specific implementationprocess, on the one hand, content in the forum is often organized bysections, and content of posts in each section usually focuses on onebig subject (e.g., a parent subject). Under this big subject, some smallsubjects (e.g., child subjects) are expanded and discussed in differentposts. For example, a big subject can be “cultural consumption”, andtherefore, many small subjects may exist tinder the subject “culturalconsumption”, and different users may make further discussions accordingto small subjects that the users are interested in. On the other hand, auser often browses the forum in a fashion of section-by-section. Whenthe users browse posts in a same section, because of the continuity ofthe interest of the users, even though the browsed posts are aboutdifferent small subjects, the distribution of the subjects of the postsis still continuous.

Based on the characteristics of browsing behavior of a user in a forum,for browsing behavior of a user within a continuous period of time,preprocessing of the browsing behavior data to obtain a secondrecommendation result includes: a post browsed by the specified user inthe forum is acquired by analyzing the browsing behavior data. Accordingto a section containing the browsed post, browsing behavior of thespecified user is categorized into at least one parent subject segment.Text similarities between titles of posts in each parent subject segmentare calculated to obtain boundaries between child subject segments ineach parent subject segment. The corresponding parent subject segment iscategorized according to the boundaries between the child subjectsegments, to obtain at least one child subject segment. According toeach child subject segment, a browsing behavior matrix of the specifieduser is established. The browsing behavior matrix includes: a childsubject and the number of appearing times that posts in the forum appearin the child subject. Calculation of relevance degree is performed onthe browsing behavior matrix to obtain the second recommendation result.

In this exemplary step, according to a section containing a browsedpost, browsing behavior is categorized into a big parent subjectsegment. Text similarities between titles of posts in each parentsubject segment are calculated to find boundaries between basic subjectsegments (or child subject segments), to further categorize each parentsubject segment into multiple child subject segments. In this manner,posts in each child subject segment may all under a same subject, andthe posts may reflect clear and single interest of a user. Next, forrecent browsing behavior of all users in each forum, a two-dimensionalmatrix can be established. One dimension in the two-dimensional matrixis a child subject segment of each user. If browsing behavior of oneuser U_(i) includes N_(i) basic subject segments, the size of thedimension corresponding to a nunmber M of users is N₁+N₂+ . . . +N_(i)+. . . +N_(M). The other dimension in the two-dimensional matrix is adimension of posts, where the value of a matrix element represents thenumber of appearing times that a post appears in one child subjectsegment.

For example. Table 1 illustrates an exemplary two-dimensional matrixinvolving four subjects and three users.

TABLE 1 Subject 1 Subject 2 Subject 3 Subject 4 User 1 1 1 0 0 User 2 01 1 0 User 3 1 0 0 1

In Table 1, a matrix element may be 1, denoting that a user has browseda corresponding subject, another matrix element may be 0, denoting thata user has not browsed a corresponding subject. Vector description ofsubject 1 is then in the column <1, 0, 1> corresponding to subject 1,and vector description of subject 2 is in the column <1, 1, 0>corresponding to subject 2.

In one embodiment, the established two-dimensionnal matrix is used as aninput to calculate the relevance degree between posts using anitem-to-item method in a classic collaborative filtering algorithm, toobtain the second recommendation result. In one embodiment, any knowncoordinated filtering algorithm may be used herein.

In Step 203: click behavior data on the recommended content of thespecified user in the forum are acquired. The click behavior data arepreprocessed to obtain a third recommendation result.

When recommending related posts, for a certain post, a system (e.g., acomputing system) may recommend several related posts to a user. In oneembodiment, click information of the specified user clicking on theserecommended posts are obtained, and modeling of a recommendation clickbehavior is performed according to the click information of the relatedposts. If each post is considered as a query and related posts of thepost are considered as query results, the click behavior data onrecommended content may be equivalent to click data in query resultswith sorted (or ranked) relevance. In one embodiment, a classicalgorithm in a click model is used to rearrange/re-sort related posts toachieve a more desirable effect.

To preprocess the click behavior data to obtain the third recommendationresult, the click behavior data are analyzed to obtain related posts ofeach post in the forum and the number of times that each related post isclicked. According to the number of times that each related post isclicked and a relationship between the time when the related post isclicked and a current time, the related posts in the forum arerearranged or re-sorted, so that a related post, having a most number ofclicks and having a difference value between the click time and thecurrent time within a preset range, is arranged/sorted in the front of aqueue, to obtain the third recommendation result.

In one embodiment, on the one hand, the rearrangement or re-sorting maybe performed according to the number of times that related posts areclicked. On the other hand, in consideration of a strong timelinesscharacteristic of data in a forum, click data for recommendations atdifferent time need to be processed differently, so that a post of whicha difference value between the click time and the current time within apreset range is arranged or sorted in the front of the queue.

As disclosed herein, a queue refers to a queue of recommendationresults. After the recommendation results are obtained, therecommendation results are placed in the queue to wait forrecommendation. The preset range may be about 5 minutes, about 10minutes, about 20 minutes, about 30 minutes, or any suitable period oftime.

It should be noted that, Steps 201 to 203 may not be implemented in anyspecific order. In one embodiment, these steps may be implemented inparallel or may be implemented in an order of time. Any suitableimplementation processes may be used in the present disclosure.

In Step 204: distribution and integration are performed on the firstrecommendation result, the second recommendation result, and the thirdrecommendation result according to weights, to obtain recommendingcontent to be recommended to the specified user.

After establishing the modeling on the above described three exemplaryuser behavior data, each modeling module outputs a correspondingrecommendation result, and distribution and integration need to beperformed on the three recommendation results. An exemplary integrationmethod may include a voting mechanism. Specifically, based on the votingmechanism, distribution and integration may be performed on the firstrecommendation result, the second recommendation result, and the thirdrecommendation result according to weights, to obtain recommendingcontent to be recommended to the specified user. For example, a firstaverage probability, a second average probability, and a third averageprobability that related posts of each post in the forum appear in thefirst recommendation result, the second recommendation result, and thethird recommendation result may be calculated respectively.

According to the first average probability, the second averageprobability, and the third average probability, the weight of the firstrecommendation result, the weight of the second recommendation result,and the weight of the third recommendation result may be determinedrespectively. Based on the weight of the first recommendation result,the weight of the second recommendation result, and the weight of thethird recommendation result, distribution and integration are performedon the first recommendation result, the second recommendation result,and the third recommendation result, to obtain the recommending contentto be recommended to the specified user.

Certainly, the integration method is not limited to the votingmechanism. In another embodiment, according to influence degree ofdifferent user behavior generated on recommendation results, differentweights may be pre-determined and provided to the recommendationresults. For example, a recommendation result outputted from a model ofrecommendation click behavior has the highest weight, a recommendationresult outputted from a model of browsing behavior has a secondaryweight (between the highest weight and the lowest weight), and arecommendation result outputted from a model of search behavior has thelowest weight. Next, a final recommending content is obtained incombination with the integration of the three recommendation resultshaving different weights.

Therefore, in an exemplary embodiment, to perform distribution andintegration on the first recommendation result, the secondrecommendation result, and the third recommendation result according toweights, to obtain recommending content to be recommended to thespecified user, distribution and integration are performed, according toa preset first weight of the first recommendation result, a presetsecond weight of the second recommendation result, and a preset thirdweight of the third recommendation result, on the first recommendationresult, the second recommendation result, and the third recommendationresult, to obtain the recommending content to be recommended to thespecified user.

In one embodiment, when integrating these results, suitable time isselected to update data in an online recommendation system, to achieve adesirable recommendation effect. Because accumulation of user behaviordata takes time, and calculation based on models of the exemplary threeuser behavior takes time, active periods (cycles) of users in each forumare analyzed to find an inactive time period of users on each forum.During such inactive time period of users, modeling calculation andresult update can be performed. For example, an analysis performed on aforum may find that less users' behavior occurs in a time period between1:00 am to 9:00 am, which can then be selected to perform calculationand update.

As such, search behavior data, browsing behavior data, and clickbehavior data on recommended content of a specified user in a forum areacquired. Preprocessing is performed on the search behavior data, thebrowsing behavior data, and the click behavior data on recommendedcontent respectively to obtain a first recommendation result, a secondrecommendation result, and a third recommendation result. Distributionand integration are performed on the first recommendation result, thesecond recommendation result, and the third recommendation resultaccording to weights, to obtain recommending content to be recommendedto the specified user. Search behavior data, browsing behavior data, andclick behavior data on recommended content are taken into comprehensiveconsideration, data used in recommendation is enriched, and accuracy ofrecommendation is improved.

Exemplary Embodiment 3

FIG. 3 illustrates an exemplary information recommendation apparatus.The exemplary apparatus may include: an acquisition module 301, apreprocessing module 302, and an integration module 303.

The acquisition module 301 is configured to acquire search behaviordata, browsing behavior data, and click behavior data on recommendedcontent of a specified user in a forum.

The preprocessing module 302 is configured to perform preprocessing onthe search behavior data, the browsing behavior data, and the clickbehavior data on recommended content respectively to obtain a firstrecommendation result, a second recommendation result, and a thirdrecommendation result.

The integration module 303 is configured to perform distribution andintegration on the first recommendation result, the secondrecommendation result, and the third recommendation result according toweights, to obtain recommending content to be recommended to thespecified user.

Referring to FIG. 4, the preprocessing module 302 includes: a filteringunit 302 a, a count unit 302 b, a first calculation unit 302 c, a firstestablishing unit 302 d, and a second calculation unit 302 f.

The filtering unit 302 a is configured to filter out noise data in thesearch behavior data to obtain noise-cancelled data. The noise-cancelleddata includes a query string input in the forum by the specified user.The count unit 302 b is configured to count the number of clickstriggered by each query string Qi on a post Ti. The first calculationunit 302 c is configured to calculate, according to the number of clickstriggered by each query string Qi on the post Ti, a click probability witriggered by each query string on the post Ti.

The first establishing unit 302 d is configured to establish, accordingto the click probability wi triggered by each query string on the postTi, a query vector for triggering a click on the post Ti, where thequery vector is <w1, w2, . . . , wi, . . . , wn>. The second calculationunit 302 f is configured to calculate, according to a query vectorestablished for each post, a relevance degree between any two posts, toobtain the first recommendation result.

Referring to FIG. 4, the preprocessing module 302 includes: anacquisition unit 302 a′, a first categorization unit 302 b′, a thirdcalculation unit 302 c′, a second categorization unit 302 d′, a secondestablishing unit 302 e′, and a fourth calculation unit 302 f.

The acquisition unit 302 a′ is configured to acquire a post browsed bythe specified user in the forum by analyzing the browsing behavior data.The first categorization unit 302 b′ is configured to categorize,according to a section containing the browsed post, browsing behavior ofthe specified user into at least one parent subject segment. The thirdcalculation unit 302 c′ is configured to calculate text similaritiesbetween titles of posts in each parent subject segment, to obtainboundaries between child subject segments in each parent subjectsegment.

The second categorization unit 302 d′ is configured to categorize acorresponding parent subject segment according to the boundaries betweenthe child subject segments, to obtain at least one child subjectsegment. The second establishing unit 302 e′ is configured to establish,according to each child subject segment, a browsing behavior matrix ofthe specified user. The browsing behavior matrix includes: a childsubject and the number of appearing times that posts in the forum appearin the child subject. The fourth calculation unit 302 f′ is configuredto perform relevance degree calculation on the browsing behavior matrix,to obtain the second recommendation result.

Referring to FIG. 4, the preprocessing module 302 includes: an analysisunit 302 a″ and a re-sorting unit 302 b″.

The analysis unit 302 a″ is configured to analyze the click behaviordata, to obtain related posts of each post in the forum and the numberof times that each related post is clicked. The re-sorting unit 302 b″is configured to re-sort, according to the number of times that eachrelated post is clicked and a relationship between a click time when therelated post is clicked and a current time, the related posts in theforum, so that a related post, having a most number of clicks and havinga difference value between the click time and the current time within apreset range, is sorted in the front of a queue, to obtain the thirdrecommendation result.

Optionally, referring to FIG. 4, the integration module 303 includes: acalculation unit 303 a, a determination unit 303 b, a first integrationunit 303 c, and a second integration unit 303 d.

The calculation unit 303 a is configured to calculate respectively afirst average probability, a second average probability, and a thirdaverage probability that related posts of each post in the forum appearin the first recommendation result, the second recommendation result,and the third recommendation result. The determination unit 303 b isconfigured to determine respectively, according to the first averageprobability, the second average probability, and the third averageprobability, the weight of the first recommendation result, the weightof the second recommendation result, and the weight of the thirdrecommendation result.

The first integration unit 303 c is configured to perform, based on theweight of the first recommendation result, the weight of the secondrecommendation result, and the weight ofthe third recommendation result,distribution and integration on the first recommendation result, thesecond recommendation result, and the third recommendation result, toobtain the recommending content to be recommended to the specified user.

Alternatively, the second integration unit 303 d is configured toperform, according to a preset first weight of the first recommendationresult, a preset second weight of the second recommendation result, anda preset third weight of the third recommendation result, distributionand integration on the first recommendation result, the secondrecommendation result, and the third recommendation result, to obtainthe recommending content to be recommended to the specified user.

As such, search behavior data, browsing behavior data, and clickbehavior data on recommended content of a specified user in a forum areacquired. Preprocessing is performed on the search behavior data, thebrowsing behavior data, and the click behavior data on recommendedcontent respectively to obtain a first recommendation result, a secondrecommendation result, and a third recommendation result. Distributionand integration are performed on the first recommendation result, thesecond recommendation result, and the third recommendation resultaccording to weights, to obtain recommending content to be recommendedto the specified user. Search behavior data, browsing behavior data, andclick behavior data on recommended content are taken into comprehensiveconsideration, data used in recommendation is enriched, and accuracy ofrecommendation is improved.

In various embodiments, the disclosed modules can be configured in oneapparatus (e.g., a processing unit) or configured in multiple apparatusas desired. The modules disclosed herein can be integrated in one moduleor in multiple modules. Each of the modules disclosed herein can bedivided into one or more sub-modules, which can be recombined in anymanner. In addition, the modules can be directly or indirectly coupledor otherwise communicated with each other, e.g., by suitable interfaces.

In the present disclosure each embodiment is progressively described,i.e., each embodiment is described and focused on difference betweenembodiments. Similar and/or the same portions between variousembodiments can be referred to with each other. In addition, exemplaryapparatus and/or systems are described with respect to correspondingmethods.

The disclosed methods, apparatus, and/or systems can be implemented in asuitable computing environment. The disclosure can be described withreference to symbol(s) and step(s) performed by one or more computers,unless otherwise specified. Therefore, steps and/or implementationsdescribed herein can be described for one or more times and executed bycomputer(s).

FIG. 5 is a schematic structural diagram of an exemplary serverconsistent with various disclosed embodiments.

The server 500 includes a central processing unit (CPU) 501, a systemmemory 504 including a random access memory (RAM) 502 and a read-onlymemory (ROM) 503, and a system bus 505 connecting the system memory 504and the central processing unit 501. The server 500 further includes abasic input/output (I/O) system 506 that assists informationtransmission between various components in a computer, and alarge-capacity storage device 507 configured to store an operatingsystem 513, an application program 514, and another program module 515.

The basic I/O system 506 includes a display 508 configured to displayinformation and an input device 509, for example, a mouse, a keyboard,configured to input information by a user. The display 508 and the inputdevice 509 are both connected to an I/O controller 510 of the system bus505 to be connected to the central processing unit 501. The basic I/Osystem 506 may further include the I/O controller 510 configured toreceive and process inputs from multiple other devices such as akeyboard, a mouse, or an electronic stylus. Similarly, the I/Ocontroller 510 further provides an output to a display screen, a printeror another type of output device.

The large-capacity storage device 507 is connected to a large-capacitystorage controller (not shown) of the system bus 505 to be connected tothe central processing unit 501. The large-capacity storage device 507and a computer readable medium correlated to the large-capacity storagedevice 507 provide the client device 500 with nonvolatile storage. Thatis, the large-capacity storage device 507 may include a computerreadable storage medium (not shown) such as a hard disk or a CD-ROMdrive.

Generally, the computer readable storage medium may include a computerstorage medium and a communications medium. The computer storage mediumincludes volatile and nonvolatile, and removable and non-removablemedium implemented in any method or technology for storage ofinformation such as a computer readable instruction, a data structure, aprogram module or other data. The computer storage medium includes, butis not limited to, a RAM, a ROM, an EEPROM, a flash memory or anothermemory technology, a CD-ROM, a digital versatile disk (DVD) or anotheroptical storage apparatus, a magnetic cassette, magnetic tape, amagnetic disk storage apparatus or other magnetic storage device.Certainly, a person skilled in the art may learn that the computerstorage medium is not limited to the several types above. The foregoingsystem memory 504 and the large-capacity storage device 507 may begenerally referred to as a memory.

According to various embodiments of the present invention, the server500 may further be executed by a remote computer that is connected to anetwork through a network such as the Internet. That is, the server 500may be connected to a network interface unit 511 on the system bus 505to be connected to a network 512, or may also be connected to anothertype of network or remote computer system (not shown) by using thenetwork interface unit 511.

The memory further includes one or more programs, and the one or moreprograms are stored in the memory, and are configured to be executed byone or more central processing units 501. The one or more programsinclude instructions configured to execute the informationrecommendation method, e.g., as shown in FIGS. 1-2.

In this manner, to improve accuracy of recommended content in a forum,the present disclosure provides a method, apparatus, and server forinformation recommendation. For example, search behavior data, browsingbehavior data, and click behavior data on recommended content of aspecified user in a forum are acquired. Preprocessing is performed onthe search behavior data, the browsing behavior data, and the clickbehavior data on recommended content respectively to obtain a firstrecommendation result, a second recommendation result, and a thirdrecommendation result. Distribution and integration are performed on thefirst recommendation result, the second recommendation result, and thethird recommendation result according to weights, to obtain recommendedcontent to be recommended to the specified user. Search behavior data,browsing behavior data, and click behavior data on recommended contentare taken into comprehensive consideration, data used in recommendationis enriched, and accuracy of recommendation is improved.

It should be understood that steps described in various methods of thepresent disclosure may be carried out in order as shown, or alternately,in a different order. Therefore, the order of the steps illustratedshould not be construed as limiting the scope of the present disclosure.In addition, certain steps may be performed simultaneously.

One of ordinary skill in the art would appreciate that suitable softwareand/or hardware may be included and used in the disclosed methods,apparatus, and/or systems. For example, the disclosed embodiments can beimplemented by hardware only, which alternatively can be implemented bysoftware products only. The software products can be stored incomputer-readable storage medium including, e.g., ROM/RAM, magneticdisk, optical disk, etc. The software products can include suitablecommands to enable a terminal device (e.g., including a mobile phone, apersonal computer, a server, or a network device, etc.) to implement thedisclosed embodiments.

The embodiments disclosed herein are exemplary only. Other applications,advantages, alternations, modifications, or equivalents to the disclosedembodiments are obvious to those skilled in the art and are intended tobe encompassed within the scope of the present disclosure.

What is claimed is:
 1. An information recommendation method, comprising:acquiring, by a server coupled to an online forum, search behavior data,browsing behavior data, and click behavior data on recommended contentof a specified user in the online forum, the server containing at leasta memory and a processor; performing preprocessing on the searchbehavior data, the browsing behavior data, and the click behavior dataon the recommended content, respectively, to obtain three recommendationresults, wherein the three recommendation results comprises a firstrecommendation result that includes first related posts corresponding toa post obtained based on the preprocessing on the search behavior data,a second recommendation result that includes second related postscorresponding to the post obtained based on the preprocessing on thebrowsing behavior data, and a third recommendation result that includesthird related posts corresponding to the post obtained based on thepreprocessing on the click behavior data; performing distribution andintegration on the first recommendation result, the secondrecommendation result, and the third recommendation result according toweights preset to each of the three recommendation results, andpresenting recommending content to be recommended to the specified userin the online forum, the recommending content being targeted to thespecified user individually, including: determining whether to include arelated post in the recommending content corresponding to the post bydetermining one or more of the three recommendation results thatincludes the related post and combining the weights preset to the one ormore of the three recommendation results; and presenting, by the server,the recommending content to the specified user, including: whendetecting that the specified user is browsing a current post of theonline forum, presenting a clickable link to a related post included inthe recommending content corresponding to the current post, wherein thesearch behavior data, the browsing behavior data, and the click behaviordata corresponding to the specified user are taken into comprehensiveconsideration in obtaining the recommending content based on thedistribution and integration of the first recommendation result, thesecond recommendation result, and the third recommendation result, suchthat data used in recommendation is enriched, and accuracy ofrecommendation is improved; wherein the step of performing preprocessingon the search behavior data to obtain the first recommendation resultcomprises: filtering out, by the processor, noise data based onmalicious clicks and robot crawling in the search behavior data toobtain noise-cancelled data such that accuracy of the recommendingcontent is improved, wherein the noise-cancelled data comprises a querystring inputted in the online forum by the specified user; counting, bythe processor, a number of clicks triggered by each query string Q_(i)on a post T_(i) of the online forum; calculating, by the processoraccording to the number of clicks triggered by each query string Q_(i)on the post T_(i), a click probability w_(i) triggered by each querystring on the post T_(i) by dividing a total number of clicks triggeredby all query strings on the post T_(i) by the number of clicks triggeredby query string Q_(i); establishing, by the processor according to theclick probability w_(i) triggered by each query string on the postT_(i), a query vector for triggering a click on the post T_(i), whereinthe query vector is <w₁, w₂, . . . , w_(i), . . . , w_(n)>, wherein bydenoting each post with the query vector composed of click probabilitiesof query strings, mapping relationships between the query strings andposts in the online forum are represented by each query vector; andcalculating, by the processor according to the query vector establishedfor each post, a relevance degree between any two posts by measuring adistance between two established query vectors corresponding to the twoposts, to obtain the first recommendation result, wherein the relevancedegree based on the established mapping relationships between the querystrings and posts in the online forum enriches data used forrecommendation; the step of performing preprocessing on the browsingbehavior data to obtain the second recommendation result comprises:acquiring a post browsed by the specified user in the forum by analyzingthe browsing behavior data; categorizing, according to a sectioncontaining the browsed post, browsing behavior of the specified userinto at least one parent subject segment; calculating text similaritiesbetween titles of posts in each parent subject segment to obtainboundaries between child subject segments in each parent subjectsegment; according to the boundaries between the child subject segmentsof a corresponding parent subject segment, categorizing thecorresponding parent subject segment to obtain at least one childsubject segment; establishing, according to each child subject segment,a browsing behavior matrix of the specified user, wherein the browsingbehavior matrix comprises: a child subject and the number of appearingtimes that posts in the forum appear in the child subject, wherein postsin each child subject segment are under a same parent subject, andreflect clear and single interest of the user; and performing arelevance degree calculation on the browsing behavior matrix, to obtainthe second recommendation result such that the second recommendationresult genuinely reflect a relationship between interest of the user andcontent of posts.
 2. The method according to claim 1, wherein the stepof performing preprocessing on the click behavior data to obtain thethird recommendation result comprises: analyzing the click behavior datato obtain related posts of each post in the forum and to obtain thenumber of times that each related post is clicked; and re-sorting,according to the number of times that each related post is clicked and arelationship between a click time when the related post is clicked and acurrent time, the related posts in the forum, so that a related post,having a large number of clicks and having a difference value betweenthe click time and the current time within a preset range, is sorted inthe front of a queue, to obtain the third recommendation result.
 3. Themethod according to claim 1, wherein the step of performing distributionand integration on the first recommendation result, the secondrecommendation result, and the third recommendation result according toweights to obtain the recommending content to be recommended to thespecified user comprises: calculating, respectively, a first averageprobability, a second average probability, and a third averageprobability that related posts of each post in the forum appear in thefirst recommendation result, the second recommendation result, and thethird recommendation result; determining, respectively, according to thefirst average probability, the second average probability, and the thirdaverage probability, a weight of the first recommendation result, aweight of the second recommendation result, and a weight of the thirdrecommendation result; and performing, based on the weight of the firstrecommendation result, the weight of the second recommendation result,and the weight of the third recommendation result, distribution andintegration on the first recommendation result, the secondrecommendation result, and the third recommendation result, to obtainthe recommending content to be recommended to the specified user.
 4. Themethod according to claim 1, wherein the step of performing distributionand integration on the first recommendation result, the secondrecommendation result, and the third recommendation result according toweights to obtain the recommending content to be recommended to thespecified user comprises: performing, according to a preset first weightof the first recommendation result, a preset second weight of the secondrecommendation result, and a preset third weight of the thirdrecommendation result, distribution and integration on the firstrecommendation result, the second recommendation result, and the thirdrecommendation result, to obtain the recommending content to berecommended to the specified user.
 5. A server, comprising: one or moreprocessors; and a storage medium coupled to the one or more processor;wherein the one or more processors are configured for: acquiring searchbehavior data, browsing behavior data, and click behavior data onrecommended content of a specified user in the online forum; performingpreprocessing on the search behavior data, the browsing behavior data,and the click behavior data on the recommended content, respectively, toobtain three recommendation results, wherein the three recommendationresults comprises a first recommendation result that includes firstrelated posts corresponding to a post obtained based on thepreprocessing on the search behavior data, a second recommendationresult that includes second related posts corresponding to the postobtained based on the preprocessing on the browsing behavior data, and athird recommendation result that includes third related postscorresponding to the post obtained based on the preprocessing on theclick behavior data; performing distribution and integration on thefirst recommendation result, the second recommendation result, and thethird recommendation result according to weights preset to each of thethree recommendation results, to obtain recommending content to berecommended to the specified user, the recommending content beingtargeted to the specified user individually, including: determiningwhether to include a related post in the recommending contentcorresponding to the post by determining one or more of the threerecommendation results that includes the related post and combining theweights preset to the one or more of the three recommendation results;and presenting, by the server, the recommending content to the specifieduser in the online forum, including: when detecting that the specifieduser is browsing a current post of the online forum, presenting aclickable link to a related post included in the recommending contentcorresponding to the current post, wherein the search behavior data, thebrowsing behavior data, and the click behavior data corresponding to thespecified user are taken into comprehensive consideration in obtainingthe recommending content based on the distribution and integration ofthe first recommendation result, the second recommendation result, andthe third recommendation result, such that data used in recommendationis enriched, and accuracy of recommendation is improved; wherein theserver further comprising instructions for performing followingoperations: filtering out noise data based on malicious clicks and robotcrawling in the search behavior data to obtain noise-cancelled data suchthat accuracy of the recommending content is improved, wherein thenoise-cancelled data comprises a query string inputted in the onlineforum by the specified user; counting a number of clicks triggered byeach query string Q_(i) on a post T_(i) of the online forum;calculating, according to the number of clicks triggered by each querystring Q_(i) on the post T_(i), a click probability w_(i) triggered byeach query string on the post T_(i) by dividing a total number of clickstriggered by all query strings on the post T_(i) by the number of clickstriggered by query string Q_(i); establishing, according to the clickprobability w_(i) triggered by each query string on the post T_(i), aquery vector for triggering a click on the post T_(i), wherein the queryvector is <w₁, w₂, . . . , w_(i), . . . , w_(n)>, wherein by denotingeach post with the query vector composed of click probabilities of querystrings, mapping relationships between the query strings and posts inthe online forum are represented by each query vector; and calculating,according to the query vector established for each post, a relevancedegree between any two posts, to obtain the first recommendation resultby measuring a distance between two established query vectorscorresponding to the two posts, wherein the relevance degree based onthe established mapping relationships between the query strings andposts in the online forum enriches data used for recommendation, whereinthe one or more processors are further configured for acquiring a postbrowsed by the specified user in the forum via analyzing the browsingbehavior data; categorizing, according to a section that the browsedpost belongs to, browsing behavior of the specified user into at leastone parent subject segment; calculating text similarities between titlesof posts in each parent subject segment, to obtain boundaries betweenchild subject segments in each parent subject segment; according to theboundaries between the child subject segments of a corresponding parentsubject segment, categorizing the corresponding parent subject segmentto obtain at least one child subject segment; establishing, according toeach child subject segment, a browsing behavior matrix of the specifieduser, wherein the browsing behavior matrix comprises: a child subjectand the number of appearing times that posts in the forum appear in thechild subject, wherein posts in each child subject segment are under asame parent subject, and reflect clear and single interest of a user;and performing a relevance degree calculation on the browsing behaviormatrix, to obtain the second recommendation result such that the secondrecommendation result genuinely reflect a relationship between interestof the user and content of posts.
 6. The server according to claim 5,wherein the one or more processors are further configured for: analyzingthe click behavior data, to obtain related posts of each post in theforum and to obtain the number of times that each related post isclicked; and re-sorting, according to the number of times that eachrelated post is clicked and a relationship between the click time whenthe related post is clicked and a current time, the related posts in theforum, so that a related post, having a most number of clicks and havinga difference value between the click time and the current time within apreset range, is sorted in the front of a queue, to obtain the thirdrecommendation result.
 7. The server according to claim 5, wherein theone or more processors are further configured for: calculating,respectively, a first average probability, a second average probability,and a third average probability that appear in the first recommendationresult, the second recommendation result, and the third recommendationresult of related posts of each post in the forum; determining,respectively, according to the first average probability, the secondaverage probability, and the third average probability, a weight of thefirst recommendation result, a weight of the second recommendationresult, and a weight of the third recommendation result; and performing,based on the weight of the first recommendation result, the weight ofthe second recommendation result, and the weight of the thirdrecommendation result, distribution and integration on the firstrecommendation result, the second recommendation result, and the thirdrecommendation result, to obtain the recommending content to berecommended to the specified user.
 8. The server according to claim 5,wherein the one or more processors are further configured for:performing, according to a preset first weight of the firstrecommendation result, a preset second weight of the secondrecommendation result, and a preset third weight of the thirdrecommendation result, distribution and integration on the firstrecommendation result, the second recommendation result, and the thirdrecommendation result, to obtain the recommending content to berecommended to the specified user.
 9. A non-transitory storage mediumhaving one or more programs stored thereon, wherein the one or moreprograms are configured to be executed by one or more processors andcomprise instructions for performing following operations: acquiringsearch behavior data, browsing behavior data, and click behavior data onrecommended content of a specified user in the online forum; performingpreprocessing on the search behavior data, the browsing behavior data,and the click behavior data on the recommended content, respectively, toobtain three recommendation results, wherein the three recommendationresults comprises a first recommendation result that includes firstrelated posts corresponding to a post obtained based on thepreprocessing on the search behavior data, a second recommendationresult that includes second related posts corresponding to the postobtained based on the preprocessing on the browsing behavior data, and athird recommendation result that includes third related postscorresponding to the post obtained based on the preprocessing on theclick behavior data; performing distribution and integration on thefirst recommendation result, the second recommendation result, and thethird recommendation result according to weights preset to each of thethree recommendation results, to obtain recommending content to berecommended to the specified user, the recommending content beingtargeted to the specified user individually, including: determiningwhether to include a related post in the recommending contentcorresponding to the post by determining one or more of the threerecommendation results that includes the related post and combining theweights preset to the one or more of the three recommendation results;and presenting, by the server, the recommending content to the specifieduser in the online forum, including: when detecting that the specifieduser is browsing a current post of the online forum, presenting aclickable link to a related post included in the recommending contentcorresponding to the current post, wherein the search behavior data, thebrowsing behavior data, and the click behavior data corresponding to thespecified user are taken into comprehensive consideration in obtainingthe recommending content based on the distribution and integration ofthe first recommendation result, the second recommendation result, andthe third recommendation result, such that data used in recommendationis enriched, and accuracy of recommendation is improved; wherein theserver further comprising instructions for performing followingoperations: filtering out noise data based on malicious clicks and robotcrawling in the search behavior data to obtain noise-cancelled data suchthat accuracy of the recommending content is improved, wherein thenoise-cancelled data comprises a query string inputted in the onlineforum by the specified user; counting a number of clicks triggered byeach query string Q_(i) on a post T_(i) of the online forum;calculating, according to the number of clicks triggered by each querystring Q_(i) on the post T_(i), a click probability w_(i) triggered byeach query string on the post T_(i) by dividing a total number of clickstriggered by all query strings on the post T_(i), by the number ofclicks triggered by query string Q_(i); establishing, according to theclick probability w_(i) triggered by each query string on the postT_(i), a query vector for triggering a click on the post T_(i), whereinthe query vector is <w₁, w₂, . . . , w_(i), . . . , w_(n)>, wherein bydenoting each post with the query vector composed of click probabilitiesof query strings, mapping relationships between the query strings andposts in the online forum are represented by each query vector; andcalculating, according to the query vector established for each post, arelevance degree between any two posts, to obtain the firstrecommendation result by measuring a distance between two establishedquery vectors corresponding to the two posts, wherein the relevancedegree based on the established mapping relationships between the querystrings and posts in the online forum enriches data used forrecommendation; wherein the server further comprising instructions forperforming following operations: acquiring a post browsed by thespecified user in the forum via analyzing the browsing behavior data;categorizing, according to a section that the browsed post belongs to,browsing behavior of the specified user into at least one parent subjectsegment; calculating text similarities between titles of posts in eachparent subject segment, to obtain boundaries between child subjectsegments in each parent subject segment; according to the boundariesbetween the child subject segments of a corresponding parent subjectsegment, categorizing the corresponding parent subject segment to obtainat least one child subject segment; establishing, according to eachchild subject segment, a browsing behavior matrix of the specified user,wherein the browsing behavior matrix comprises: a child subject and thenumber of appearing times that posts in the forum appear in the childsubject, wherein posts in each child subject segment are under a sameparent subject, and reflect clear and single interest of the user; andperforming a relevance degree calculation on the browsing behaviormatrix, to obtain the second recommendation result such that the secondrecommendation result genuinely reflect a relationship between interestof the user and content of posts.
 10. The storage medium according toclaim 9, further comprising instructions for performing followingoperations: analyzing the click behavior data, to obtain related postsof each post in the forum and to obtain the number of times that eachrelated post is clicked; and re-sorting, according to the number oftimes that each related post is clicked and a relationship between theclick time when the related post is clicked and a current time, therelated posts in the forum, so that a related post, having a most numberof clicks and having a difference value between the click time and thecurrent time within a preset range, is sorted in the front of a queue,to obtain the third recommendation result.
 11. The storage mediumaccording to claim 9, further comprising instructions for performingfollowing operations: calculating, respectively, a first averageprobability, a second average probability, and a third averageprobability that appear in the first recommendation result, the secondrecommendation result, and the third recommendation result of relatedposts of each post in the forum; determining, respectively, according tothe first average probability, the second average probability, and thethird average probability, a weight of the first recommendation result,a weight of the second recommendation result, and a weight of the thirdrecommendation result; and performing, based on the weight of the firstrecommendation result, the weight of the second recommendation result,and the weight of the third recommendation result, distribution andintegration on the first recommendation result, the secondrecommendation result, and the third recommendation result, to obtainthe recommending content to be recommended to the specified user. 12.The storage medium according to claim 9, further comprising instructionsfor performing following operations: performing, according to a presetfirst weight of the first recommendation result, a preset second weightof the second recommendation result, and a preset third weight of thethird recommendation result, distribution and integration on the firstrecommendation result, the second recommendation result, and the thirdrecommendation result, to obtain the recommending content to berecommended to the specified user.
 13. The storage medium according toclaim 9, further comprising instructions for performing followingoperations: analyzing active periods of users in the online forum tofind an inactive time period; accumulating the search behavior data, thebrowsing behavior data, and the click behavior data on the recommendedcontent of the specified user during a time period other than theinactive time period; and performing the distribution and integration onthe first recommendation result, the second recommendation result, andthe third recommendation result during the inactive time period.